Create app.py

app.py

@@ -0,0 +1,368 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import spaces
+from duckduckgo_search import DDGS
+import time
+import torch
+from datetime import datetime
+
+# Initialize model and tokenizer
+model_name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+tokenizer.pad_token = tokenizer.eos_token
+
+# Modified model loading for CPU
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="cpu",  # Changed to CPU
+    low_cpu_mem_usage=True,
+    torch_dtype=torch.float32  # Changed to float32 for CPU
+)
+
+def get_web_results(query, max_results=5):  # Increased to 5 for better context
+    """Get web search results using DuckDuckGo"""
+    try:
+        with DDGS() as ddgs:
+            results = list(ddgs.text(query, max_results=max_results))
+            return [{
+                "title": result.get("title", ""),
+                "snippet": result["body"],
+                "url": result["href"],
+                "date": result.get("published", "")
+            } for result in results]
+    except Exception as e:
+        return []
+
+def format_prompt(query, context):
+    """Format the prompt with web context"""
+    current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    context_lines = '\n'.join([f'- [{res["title"]}]: {res["snippet"]}' for res in context])
+    return f"""You are an intelligent search assistant. Answer the user's query using the provided web context.
+Current Time: {current_time}
+
+Query: {query}
+
+Web Context:
+{context_lines}
+
+Provide a detailed answer in markdown format. Include relevant information from sources and cite them using [1], [2], etc.
+Answer:"""
+
+def format_sources(web_results):
+    """Format sources with more details"""
+    if not web_results:
+        return "<div class='no-sources'>No sources available</div>"
+    
+    sources_html = "<div class='sources-container'>"
+    for i, res in enumerate(web_results, 1):
+        title = res["title"] or "Source"
+        date = f"<span class='source-date'>{res['date']}</span>" if res['date'] else ""
+        sources_html += f"""
+        <div class='source-item'>
+            <div class='source-number'>[{i}]</div>
+            <div class='source-content'>
+                <a href="{res['url']}" target="_blank" class='source-title'>{title}</a>
+                {date}
+                <div class='source-snippet'>{res['snippet'][:150]}...</div>
+            </div>
+        </div>
+        """
+    sources_html += "</div>"
+    return sources_html
+
+def generate_answer(prompt):
+    """Generate answer using the DeepSeek model"""
+    inputs = tokenizer(
+        prompt, 
+        return_tensors="pt", 
+        padding=True,
+        truncation=True,
+        max_length=256,  # Reduced max length for CPU
+        return_attention_mask=True
+    )  # Removed .to(model.device) since we're using CPU
+    
+    outputs = model.generate(
+        inputs.input_ids,
+        attention_mask=inputs.attention_mask,
+        max_new_tokens=128,  # Reduced for faster generation on CPU
+        temperature=0.7,
+        top_p=0.95,
+        pad_token_id=tokenizer.eos_token_id,
+        do_sample=True,
+        early_stopping=True,
+        num_beams=1  # Reduced beam search for faster generation
+    )
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+def process_query(query, history):
+    """Process user query with streaming effect"""
+    try:
+        if history is None:
+            history = []
+            
+        # Get web results first
+        web_results = get_web_results(query)
+        sources_html = format_sources(web_results)
+        
+        current_history = history + [[query, "*Searching...*"]]
+        yield {
+            answer_output: gr.Markdown("*Searching the web...*"),
+            sources_output: gr.HTML(sources_html),
+            search_btn: gr.Button("Searching...", interactive=False),
+            chat_history_display: current_history
+        }
+        
+        # Generate answer
+        prompt = format_prompt(query, web_results)
+        answer = generate_answer(prompt)
+        final_answer = answer.split("Answer:")[-1].strip()
+        
+        updated_history = history + [[query, final_answer]]
+        yield {
+            answer_output: gr.Markdown(final_answer),
+            sources_output: gr.HTML(sources_html),
+            search_btn: gr.Button("Search", interactive=True),
+            chat_history_display: updated_history
+        }
+    except Exception as e:
+        error_message = str(e)
+        if "GPU quota" in error_message:
+            error_message = "⚠️ GPU quota exceeded. Please try again later when the daily quota resets."
+        
+        yield {
+            answer_output: gr.Markdown(f"Error: {error_message}"),
+            sources_output: gr.HTML(sources_html),
+            search_btn: gr.Button("Search", interactive=True),
+            chat_history_display: history + [[query, f"*Error: {error_message}*"]]
+        }
+
+# Update the CSS for better contrast and readability
+css = """
+.gradio-container {
+    max-width: 1200px !important;
+    background-color: #f7f7f8 !important;
+}
+
+#header {
+    text-align: center;
+    margin-bottom: 2rem;
+    padding: 2rem 0;
+    background: #1a1b1e;
+    border-radius: 12px;
+    color: white;
+}
+
+#header h1 {
+    color: white;
+    font-size: 2.5rem;
+    margin-bottom: 0.5rem;
+}
+
+#header h3 {
+    color: #a8a9ab;
+}
+
+.search-container {
+    background: #1a1b1e;
+    border-radius: 12px;
+    box-shadow: 0 4px 12px rgba(0,0,0,0.1);
+    padding: 1rem;
+    margin-bottom: 1rem;
+}
+
+.search-box {
+    padding: 1rem;
+    background: #2c2d30;
+    border-radius: 8px;
+    margin-bottom: 1rem;
+}
+
+/* Style the input textbox */
+.search-box input[type="text"] {
+    background: #3a3b3e !important;
+    border: 1px solid #4a4b4e !important;
+    color: white !important;
+    border-radius: 8px !important;
+}
+
+.search-box input[type="text"]::placeholder {
+    color: #a8a9ab !important;
+}
+
+/* Style the search button */
+.search-box button {
+    background: #2563eb !important;
+    border: none !important;
+}
+
+/* Results area styling */
+.results-container {
+    background: #2c2d30;
+    border-radius: 8px;
+    padding: 1rem;
+    margin-top: 1rem;
+}
+
+.answer-box {
+    background: #3a3b3e;
+    border-radius: 8px;
+    padding: 1.5rem;
+    color: white;
+    margin-bottom: 1rem;
+}
+
+.answer-box p {
+    color: #e5e7eb;
+    line-height: 1.6;
+}
+
+.sources-container {
+    margin-top: 1rem;
+    background: #2c2d30;
+    border-radius: 8px;
+    padding: 1rem;
+}
+
+.source-item {
+    display: flex;
+    padding: 12px;
+    margin: 8px 0;
+    background: #3a3b3e;
+    border-radius: 8px;
+    transition: all 0.2s;
+}
+
+.source-item:hover {
+    background: #4a4b4e;
+}
+
+.source-number {
+    font-weight: bold;
+    margin-right: 12px;
+    color: #60a5fa;
+}
+
+.source-content {
+    flex: 1;
+}
+
+.source-title {
+    color: #60a5fa;
+    font-weight: 500;
+    text-decoration: none;
+    display: block;
+    margin-bottom: 4px;
+}
+
+.source-date {
+    color: #a8a9ab;
+    font-size: 0.9em;
+    margin-left: 8px;
+}
+
+.source-snippet {
+    color: #e5e7eb;
+    font-size: 0.9em;
+    line-height: 1.4;
+}
+
+.chat-history {
+    max-height: 400px;
+    overflow-y: auto;
+    padding: 1rem;
+    background: #2c2d30;
+    border-radius: 8px;
+    margin-top: 1rem;
+}
+
+.examples-container {
+    background: #2c2d30;
+    border-radius: 8px;
+    padding: 1rem;
+    margin-top: 1rem;
+}
+
+.examples-container button {
+    background: #3a3b3e !important;
+    border: 1px solid #4a4b4e !important;
+    color: #e5e7eb !important;
+}
+
+/* Markdown content styling */
+.markdown-content {
+    color: #e5e7eb !important;
+}
+
+.markdown-content h1, .markdown-content h2, .markdown-content h3 {
+    color: white !important;
+}
+
+.markdown-content a {
+    color: #60a5fa !important;
+}
+
+/* Accordion styling */
+.accordion {
+    background: #2c2d30 !important;
+    border-radius: 8px !important;
+    margin-top: 1rem !important;
+}
+"""
+
+# Update the Gradio interface layout
+with gr.Blocks(title="AI Search Assistant", css=css, theme="dark") as demo:
+    chat_history = gr.State([])
+    
+    with gr.Column(elem_id="header"):
+        gr.Markdown("# 🔍 AI Search Assistant")
+        gr.Markdown("### Powered by DeepSeek & Real-time Web Results")
+    
+    with gr.Column(elem_classes="search-container"):
+        with gr.Row(elem_classes="search-box"):
+            search_input = gr.Textbox(
+                label="", 
+                placeholder="Ask anything...", 
+                scale=5,
+                container=False
+            )
+            search_btn = gr.Button("Search", variant="primary", scale=1)
+        
+        with gr.Row(elem_classes="results-container"):
+            with gr.Column(scale=2):
+                with gr.Column(elem_classes="answer-box"):
+                    answer_output = gr.Markdown(elem_classes="markdown-content")
+                with gr.Accordion("Chat History", open=False, elem_classes="accordion"):
+                    chat_history_display = gr.Chatbot(elem_classes="chat-history")
+            with gr.Column(scale=1):
+                with gr.Column(elem_classes="sources-box"):
+                    gr.Markdown("### Sources")
+                    sources_output = gr.HTML()
+        
+        with gr.Row(elem_classes="examples-container"):
+            gr.Examples(
+                examples=[
+                    "What are the latest developments in quantum computing?",
+                    "Explain the impact of AI on healthcare",
+                    "What are the best practices for sustainable living?",
+                    "How is climate change affecting ocean ecosystems?"
+                ],
+                inputs=search_input,
+                label="Try these examples"
+            )
+
+    # Handle interactions
+    search_btn.click(
+        fn=process_query,
+        inputs=[search_input, chat_history],
+        outputs=[answer_output, sources_output, search_btn, chat_history_display]
+    )
+    
+    # Also trigger search on Enter key
+    search_input.submit(
+        fn=process_query,
+        inputs=[search_input, chat_history],
+        outputs=[answer_output, sources_output, search_btn, chat_history_display]
+    )
+
+if __name__ == "__main__":
+    demo.launch(share=True)